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Turns out there are some optimizations you can do when compiling Python 3.5 and 3.6 that give you some significant speed improvements without any real downside. We've paired with with Google's base Debian image that rips out systemd and all it's dependencies which yields a MUCH smaller image.

It could be challenging to pick the right data visualization tool in Python. There are so many options available. Summarizing most common tools, then testing and comparing different techniques would help to pick the best fit and method for the needed visualization.

Using user review data from Yelp, our aim was to develop a recommendation system to provide a new restaurant suggestion that a user might like. The project was motivated by the fact that recommendation problems are ubiquitious, including the infamous Netflix challenge and "similar items you might like" suggestions from Amazon. Given the diverse application of this problem, we wanted to learn how to develop and implement such system using machine learning.

Yesterday, Geoffrey released html5lib 1.0 [1]! The changes aren't wildly interesting.The more interesting part for me is how the release happened. I'm going to spend the rest of this post talking about that. Those looking to manage / understand the workings behind becoming a maintainer of a open source project should read this.

Bitcoin is now mainstream. While the debate on whether it’s a speculative bubble or the greatest thing since the internet continues, one thing that is irrefutable is that it has attracted a significant amount of investor interest to cryptocurrency and digital assets.

The pymatch Python package implements Propensity Score Matching (PSM) techniques intended for use with observational study designs. It was inspired by and adapted from Jasjeet Singh Sekhon’s Matching package in R. I wrote an adaptation in Python that is better suited for my work at Mozilla